TY - JOUR
T1 - Leveraging the wisdom of the crowd to address societal challenges
T2 - Revisiting the knowledge reuse for innovation process through analytics
AU - Han, Yue
AU - Ozturk, Pinar
AU - Nickerson, Jeffrey V.
N1 - Publisher Copyright:
© 2020 by the Association for Information Systems.
PY - 2020
Y1 - 2020
N2 - Societal challenges can be addressed not only by experts but also by crowds. Crowdsourcing provides a way to engage a crowd to contribute to the solutions of some of the biggest challenges of our era: how to cut our carbon footprint, how to address worldwide epidemic of chronic disease, and how to achieve sustainable development. Isolated crowd-based solutions in online communities are not always creative and innovative. Hence, remixing has been developed as a way to enable idea evolution and integration, and to harness reusable innovative solutions. Understanding the generativity of remixing is essential to leveraging the wisdom of the crowd to solve societal challenges. At its best, remixing can promote online community engagement, as well as support comprehensive and innovative solution generation. Organizers can maintain an active online community, community members can collectively innovate and learn, and, as a result, society can find new ways to solve important problems. We address what affects the generativity of a remix by revisiting the knowledge reuse for innovation process model. We analyze the reuse of proposals in Climate CoLab, an online innovation community that aims to address global climate change issues. Our application of several analytical methods to study factors that may contribute to the generativity of a remix reveals that remixes that include prevalent topics and integration metaknowledge are more generative. We conclude by suggesting strategies and tools that can help online communities better harness collective intelligence for addressing societal challenges.
AB - Societal challenges can be addressed not only by experts but also by crowds. Crowdsourcing provides a way to engage a crowd to contribute to the solutions of some of the biggest challenges of our era: how to cut our carbon footprint, how to address worldwide epidemic of chronic disease, and how to achieve sustainable development. Isolated crowd-based solutions in online communities are not always creative and innovative. Hence, remixing has been developed as a way to enable idea evolution and integration, and to harness reusable innovative solutions. Understanding the generativity of remixing is essential to leveraging the wisdom of the crowd to solve societal challenges. At its best, remixing can promote online community engagement, as well as support comprehensive and innovative solution generation. Organizers can maintain an active online community, community members can collectively innovate and learn, and, as a result, society can find new ways to solve important problems. We address what affects the generativity of a remix by revisiting the knowledge reuse for innovation process model. We analyze the reuse of proposals in Climate CoLab, an online innovation community that aims to address global climate change issues. Our application of several analytical methods to study factors that may contribute to the generativity of a remix reveals that remixes that include prevalent topics and integration metaknowledge are more generative. We conclude by suggesting strategies and tools that can help online communities better harness collective intelligence for addressing societal challenges.
KW - Climate Change
KW - Collective Intelligence
KW - Innovation
KW - Knowledge Reuse
KW - Online Communities
KW - Remixing
KW - Societal Challenges
UR - http://www.scopus.com/inward/record.url?scp=85090874225&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090874225&partnerID=8YFLogxK
U2 - 10.17705/1jais.00632
DO - 10.17705/1jais.00632
M3 - Article
AN - SCOPUS:85090874225
SN - 1536-9323
VL - 21
SP - 1128
EP - 1152
JO - Journal of the Association for Information Systems
JF - Journal of the Association for Information Systems
IS - 5
ER -